AMERICAN JOURNAL OF INDUSTRIAL MEDICINE 57:695–708 (2014)
Psychosocial Factors at Work and Sickness Absence: Results from the French National SUMER Survey Thomas Lesuffleur,
1,2 MSc,
Jean-François Chastang, PhD,1,2 Nicolas Sandret, and Isabelle Niedhammer, PhD1,2
MD,
3
Objective This study aims at exploring the associations between psychosocial work factors and sickness absence. Methods The sample from the French National Survey SUMER 2010 included 46,962 employees. Sickness absence spells and days within the last year were studied as two outcomes. Psychosocial work factors included psychological demands, decision latitude, social support, reward, working time, and workplace violence variables. Covariates were age, occupation, economic activity, and other occupational exposures. Results For both genders, low latitude, low reward, shift work, bullying, and verbal abuse were risk factors of absence spells while long working hours were a protective factor. High demands, low support, and physical violence were risk factors for women. Low support and bullying for both genders, high demands for women, and low reward, long working hours, and physical violence for men increased absence duration. Conclusions This study highlights the importance of psychosocial work factors as risk factors of sickness absence. Am. J. Ind. Med. 57:695–708, 2014. ß 2014 Wiley Periodicals, Inc. KEY WORDS: psychosocial work factors; job stress; workplace violence, sickness absence; France
INTRODUCTION Sickness absence is recognized as a global indicator of health status, and as a marker of social, psychological, and physical functioning for working populations [Marmot et al., 1995; Kivimaki et al., 2003b]. The occurrence of sickness
1 INSERM,UMR_S1136,Pierre Louis Institute of Epidemiologyand PublicHealth,Department of Social Epidemiology, F-75013, Paris, France 2 Sorbonne Universite´s, UPMC Univ Paris 06, UMR_S1136, Pierre Louis Institute of Epidemiology and Public Health, Department of Social Epidemiology, F-75013, Paris, France 3 Inspection Me´dicale duTravail, Paris, France Contract grant sponsor: French Ministry of Labour (DARES); Contract grant number: 2200684049. Disclosure Statement: The authors report no conflicts of interests. Correspondence to: Dr. Isabelle Niedhammer, PhD, INSERM UMRS 1136çIPLESP, Team 7 (ERES), Faculte´ de Me´decine Pierre et Marie Curieçp^ole Saint-Antoine, 27 rue de Chaligny, 75012 Paris, France. E-mail: [email protected]
Accepted13 February 2014 DOI10.1002/ajim.22317. Published online17 March 2014 in Wiley Online Library (wileyonlinelibrary.com).
ß 2014 Wiley Periodicals, Inc.
absence may be influenced by various factors, such as social, personal, and demographic characteristics, exposures from work environment and conditions specific to a given workplace or organization [Prins and de Graaf, 1986; Alexanderson, 1998; Niedhammer et al., 1998]. Because sickness absence is strongly related to health and has an important economic impact in terms of costs for health insurance and loss of productivity, identification of the factors associated with this outcome becomes essential as a guide to preventive measures. Work-related factors and especially psychosocial work factors have an important impact on worker’s health and may play a role in the occurrence of sickness absence. To evaluate psychosocial work factors, theoretical models have appeared in the literature within the last decades. The job strain model, developed by Karasek et al. [1998], is probably the most used theoretical model of job stress and is composed of three main dimensions, psychological demands, decision latitude including two subdimensions, skill discretion and decision authority, and
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social support at work from colleagues and supervisors. Job strain defined by the combination of high demands and low latitude is considered to be the most detrimental situation, as well as iso-strain, the combination of job strain and low support. Another model, the effort–reward imbalance model focuses on effort and reward at work in terms of esteem, job promotion, and job security [Siegrist et al., 2004]. Other concepts have emerged more recently: those related to workplace violence, such as physical violence [Loomis et al., 2001], sexual harassment [Sbraga and O’Donohue, 2000], verbal abuse and bullying [Einarsen, 2000], working hours/ time such as long working hours [Van der Hulst, 2003], predictability [Vaananen et al., 2008], shift work, night work, and asocial work days [Merkus et al., 2012], as well as demands for responsibility [Pejtersen et al., 2010]. Studies reported an increased risk of sickness absence among workers exposed to high levels of psychological demands, low-decision latitude, and low-social support [Niedhammer et al., 1998; Gimeno et al., 2004; Moreau et al., 2004]. For other recent concepts, the literature remains sparse. Factors like low-job promotion, long working hours, shift work, or bullying were found to be associated with sickness absence [Kivimaki et al., 2000; Voss et al., 2001; Merkus et al., 2012; Hinkka et al., 2013; Niedhammer et al., 2013]. Furthermore, only few studies explored a large set of psychosocial work factors, and when it was the case, these studies focused on specific working populations (i.e., specific occupations or work sectors) [Eriksen et al., 2003; Lund et al., 2005; Roelen et al., 2009]. Moreover, most of these studies did not always take important covariates into account, especially those related to the physical working environment. In the present study, we measured sickness absence by the number of sickness absence spells and also by the number of absence days in order to study the differences in the associations of psychosocial work conditions with sickness absence spells and days. This study was based on a large French database, examined various measures of psychosocial work factors following well-known theoretical models and emergent concepts, and also included occupational exposures of physical, chemical, biomechanical, and biological nature. The objectives of the study were to explore the associations between a large number of psychosocial work factors and sickness absence (both spells and days) in a large national representative sample of French employees.
METHODS Study Population The SUMER survey is a periodical national survey from the DARES (French Ministry of Labour) conducted every 7 years. Its objective is to evaluate all kinds of occupational exposures among the national working population of
employees, in order to define preventive strategies and research priorities in France. SUMER 2010, the last survey conducted in 2010, included around 50,000 employees interviewed about their physical, biological, chemical, biomechanical, and organizational/psychosocial exposures by 2,400 occupational physicians. Employees also completed a self-administered questionnaire in which their responses were collected about the job strain and reward questionnaires, as well as about other psychosocial work factors such as workplace violence. The SUMER survey was approved by the French Ethics Committees called Commission Nationale de l’Informatique et des Libertés—CNIL- and Conseil National de l’Information Statistique—CNIS (informed consent was not necessary for this survey).
Sickness Absence Data Sickness absence was provided by the following two items: the number of spells of absence (0, 1, 2, or 3 and more) for health-related reasons (excluding work accident or maternity) within the last 12 months and the total number of absence days within the same period of time. Consequently, two variables were used: a variable with four categories representing the number of sickness absence spells within the past 12 months and a variable representing the number of sickness absence days for the employees with at least one absence spell within the last 12 months.
Psychosocial Factors at Work Twenty-two psychosocial work factors were constructed. Karasek’s job stress dimensions were constructed using the validated French version of the questionnaire [Niedhammer, 2002; Niedhammer et al., 2006]: decision latitude (nine items, Cronbach alpha ¼ 0.78, including six items for skill discretion, three items for decision authority), psychological demands (nine items, Cronbach alpha ¼ 0.80) and social support (eight items, Cronbach alpha ¼ 0.82, including four items for social support from colleagues and four items for social support from supervisors). The scores were constructed according to the recommendations by Karasek and dichotomized at the median of the total sample [Karasek et al., 1998]. Job strain was defined by the combination of high demands and low latitude, and iso-strain by the combination of job strain and low support. Reward (11 items, Cronbach alpha ¼ 0.85, including 5 items for esteem, 2 items for job security and 4 items for job promotion) from the effort–reward imbalance model was measured using the validated French version of this questionnaire [Niedhammer et al., 2000]. Reward and its sub-dimensions were dichotomized at the median of the total sample.
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The following five working time variables were studied: long working hours (one item, 48 hr/week following the European Directive on working time), night work (one item, working between 12 and 5 a.m. 1 night/week), shift work (one item, either permanent or alternating/rotating shifts), asocial work days (one item, working on Sundays or Saturdays once/week) and predictability of schedules (four items, information about time schedules for the next day, week, month, and the next 3 months). Shift and night work were included in this group of factors; they are recognized as chronobiological risk factors for a number of health outcomes but they may also been seen as psychosocial work factors as they are strongly related to work organization and may also disturb social and family life. Three factors were related to workplace violence: physical violence or sexual assault (two items), bullying (one item), and verbal abuse (two items). Exposure was defined by at least one situation of workplace violence for each factor, when the measure was based on more than one item. Demands for responsibility (four items: a mistake in work may lead to serious consequences for product/service quality, to serious financial losses for the company, dangerous consequences for the safety of people or yourself, and to wage/work/job sanctions for yourself) were dichotomized at the median of the total sample.
Covariates Several covariates, age, occupation coded using the French classification of occupation (PCS-INSEE) which is close to the International Standard Classification of Occupation (ISCO), economic activity of the company coded using the European classification (NACE) and four other occupational exposures (physical, biomechanical, biological, and chemical exposures) were included. Physical exposure was defined by at least 20 hr of exposure to noise (impulses or other disturbing noises), thermic constraints (or humidity) or radiation, within the previous week. Biomechanical exposure was defined by at least 20 hr of exposure to manual materials handling, postural and articular constraints, vibrations (manual handling of vibrating tools and vibration from a fixed machine) or driving (driving of specialized machinery, car, bus) within the previous week. Biological exposure was defined by at least one biological exposure within the previous week. Chemical exposure was defined by at least one chemical exposure within the previous week. These covariates were chosen because they were found to be associated with sickness absence in the literature [Prins and de Graaf, 1986; Messing et al., 1998; Boedeker, 2001; Fuhrer et al., 2002] and may also be associated with psychosocial work factors. Consequently, adjusting for these
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covariates may be useful in the study of the associations between psychosocial work factors and sickness absence.
Statistical Methods The description of the sample was provided and the differences between genders for all variables were tested using the chi-square and the Student’s t-test. As the variable representing the number of sickness absence spells contains four categories, it was studied using generalized logit regression model that allowed to study the risk of having at least one absence spell but also the risk associated with the increase in the number of sickness absence spells. This model produces odds ratios (ORs), that is, odds of having one (respectively two, three, or more) sickness absence spell associated with each independent variable. The number of sickness absence days is a form of count data, and the association between psychosocial work factors and sickness absence days was studied among the subsample of employees who had at least 1 day of absence using negative binomial regression model. This model produces mean ratios (MRs) for the number of absence days associated with each independent variable. Full details about these models may be found elsewhere [Niedhammer et al., 2013]. The two outcomes were thus used to perform two different analyses, a first analysis on the whole sample to identify the factors associated with the presence of sickness absence spells, and a second analysis on the sub-sample of those with at least one absence day to identify the factors associated with the duration of absence. First, all psychosocial work factors were studied separately with adjustment for covariates (models I). Then, all the psychosocial work factors significant for at least one gender were included simultaneously as independent variables in a final model with adjustment for covariates (models II). Although associations were found between psychosocial work factors, no colinearity was detected in models II. An interaction term between decision latitude and psychological demands was tested following Karasek’s hypothesis, but no significant interaction was found. Men and women were analyzed separately, and all analyses were performed using SAS.
RESULTS Description The sample included 46,962 employees, 26,883 men and 20,079 women. The response rate was 87%. Significant differences were observed between genders for age, occupation, economic activity, skill discretion, decision authority, decision latitude, psychological demands, job strain, isostrain, esteem, insecurity, long working hours, night work, shift work, predictability, physical violence/sexual assault,
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bullying, verbal abuse, demands for responsibility, and other occupational exposures. The description of the sample is shown in Table I, according to gender for all studied variables. The rate of absence spells was significantly higher for women than for men. There was no significant difference in the mean of sickness absence days: 16.6 days for men (standard deviation: 32.9) and 17.5 days for women (standard deviation: 34.2) among the sample of those having at least one absence day within the last year.
Generalized Logit and Negative Binomial Models (Models I) Tables II and III provide the results of the associations between psychosocial work factors and sickness absence, each factor being studied separately with adjustment for covariates. For both genders, almost all psychosocial work factors were significantly associated with sickness absence spells except night work and asocial work hours for both genders, demands for responsibility for men and predictability for women (Table II). Among all these psychosocial work factors, only long working hours for both genders and low predictability for men were protective factors. For most of these associations (Table II), the association was stronger with the increase of the number of absence spells. For example, for men, the ORs associated with bullying was 1.31 for one sickness absence spell, 1.98 for two sickness absence spells and 3.15 for three or more sickness absence spells. The risk increased significantly from one category to another. Concerning the number of sickness absence days among the sub-sample of employees with sickness absence (Table III), the number of significant associations was lower: skill discretion, psychological demands, social support from colleagues, social support, job strain, iso-strain, esteem, insecurity, job promotion, reward, physical violence/sexual assault, bullying, and verbal abuse for both genders and decision authority, decision latitude and long working hours for men were associated with sickness absence days. The strongest association was found for physical violence/sexual assault for both genders: men exposed to physical violence/sexual assault had 37% more absence days than men who were not exposed, and women exposed to physical violence/sexual assault had 29% more absence days than non-exposed women. Night work and asocial work hours were the only two variables that were not associated with sickness absence (both spells and days) for both genders. Concerning the associations between covariates and sickness absence spells, all covariates were significant except chemical exposure for men and biological exposure for
women. Sickness absence spells were significantly higher for low-skilled occupations and younger employees. In addition, regarding the number of sickness absence days, older employees were more likely to have longer absences than younger employees (not shown).
Generalized Logit and Negative Binomial Models (Models II) Tables IV and V show the associations of all psychosocial work factors included simultaneously in the same model with adjustment for covariates. The factors associated with sickness absence spells (Table IV) were low-decision latitude, low reward, shift work, bullying, and verbal abuse for both genders and highpsychological demands, low-social support and physical violence/sexual assault for women. Long working hours were a protective factor of sickness absence spells for both genders and low predictability was a protective factor for men. Regarding the results for the sub-dimensions of decision latitude and reward, low skill discretion, low-decision authority, low esteem, job insecurity, and low-job promotion were risk factors of sickness absence spells for both genders (not shown). Among the sub-sample of employees with sickness absence, the number of sickness absence days (Table V) increased with low-social support and bullying for both genders, low reward, long working hours, and physical violence/sexual assault for men and high-psychological demands for women. The strongest association was found for physical violence/sexual assault (MR ¼ 1.31) for men. Regarding covariates, being younger, low-skilled occupations, physical, biomechanical (except for women), and biological exposure (except for women) were found to be risk factors of sickness absence spells. For the duration of sickness absence, being older, low-skilled occupations, biomechanical exposure for men, and physical exposure for women were found to be risk factors (not shown).
DISCUSSION Main Results In this study, we analyzed the associations of twenty-two psychosocial work factors with sickness absence spells and days among both genders. The multivariate analysis, including all psychosocial work factors together with adjustment for covariates, showed that low-decision latitude, low reward, shift work, bullying, and verbal abuse were risk factors for sickness absence spells while long working hours was a protective factor for both genders. High-psychological demands, low-social support, and physical violence/sexual assault for women were risk factors of sickness absence
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TABLE I. Study Population, French SUMER National Survey, 2010 Men (N ¼ 26,883), N (%) Age (years) 48 hr/week) Night work Non-exposed Exposed Shift work Non-exposed Exposed Asocial workdays Non-exposed Exposed Predictability High Low Physical violence/sexual assault Non-exposed Exposed Bullying Non-exposed Exposed Verbal abuse Non-exposed Exposed Demands for responsibility Low High Chemical exposure Non-exposed Exposed Biological exposure Non-exposed Exposed Physical exposure Non-exposed Exposed Biomechanical exposure Non-exposed Exposed Sickness absence spells 0 1 2
Men (N ¼ 26,883), N (%)
Women (N ¼ 20,079), N (%)
15,161 (57.0) 11,439 (43.0)
11,063 (56.2) 8,623 (43.8)
13,002 (48.9) 13,603 (51.1)
9,634 (48.8) 10,117 (51.2)
P-value NS
NS
24,097 (90.2) 2,614 (9.8)
19,195 (96.3) 734 (3.7)
24,203 (91.6) 2,214 (8.4)
19,356 (97.2) 558 (2.8)
21,261 (79.5) 5,492 (20.5)
16,805 (84.1) 3,189 (15.9)
21,178 (78.8) 5,680 (21.2)
16,564 (82.5) 3,503 (17.5)
18,049 (67.3) 8,766 (32.7)
14,009 (69.9) 6,017 (30.1)
25,813 (98.5) 402 (1.5)
19,112 (98.1) 375 (1.9)
20,881 (77.7) 6,002 (22.3)
15,324 (76.3) 4,755 (23.7)
21,208 (80.2) 5,224 (19.8)
14,362 (72.8) 5,375 (27.2)
11,388 (42.4) 15,574 (57.6)
13,213 (65.9) 6,843 (34.1)
16,597 (61.7) 10,286 (38.3)
14,706 (73.2) 5,373 (26.8)
22,601 (84.1) 4,282 (15.9)
14,120 (70.3) 5,959 (29.7)
20,633 (76.7) 6,250 (23.3)
17,881 (89.1) 2,198 (10.9)
12,566 (46.7) 14,317 (53.3)
11,471 (57.1) 8,608 (42.9)
18,133 (68.3) 6,016 (22.7) 1,722 (6.5)
12,319 (62.0) 5,009 (25.2) 1,685 (8.5) (Continued )
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TABLE I. (Continued.)
3 Sickness absence days
Men (N ¼ 26,883), N (%)
Women (N ¼ 20,079), N (%)
P-value
667 (2.5) Men (N ¼ 7,768), mean (SD) 16.6 (32.9)
844 (4.2) Women (N ¼ 6,978), mean (SD) 17.5 (34.2)
NS
NS, not significant. P < 0.05. P < 0.01. P < 0.001 (chi-square and student test for the comparison between men and women).
spells. Regarding the number of sickness absence days among the sub-sample of employees with sickness absence, male and female employees exposed to low-social support and bullying were more likely to have longer absence. In addition, low reward, long working hours, and physical violence/sexual assault among men and high-psychological demands among women increased the number of absence days. Other results from less conservative models (including each factor separately and not together) provided a higher number of factors associated with sickness absence spells and days.
Strengths and Limitations of the Study The study was based on a large national representative sample of French employees, and the response rate was very satisfactory (87%), making a participation bias unlikely. The large sample size allowed to perform separate analysis for men and women. The study included a large variety of psychosocial work factors, with both classical and emergent factors. The validated French version of the questionnaires for measuring psychological demands, decision latitude, social support, and reward were used. Furthermore, we used two measures of sickness absence, spells and days of sickness absence, making the study of the differences in the associations between psychosocial work conditions and these two outcomes possible. For the analysis of sickness absence spells, we used generalized logit regression model that allowed us to study the risk of having at least one absence spell but also the increase of risk associated with the number of absence spells. We also took important covariates into account, some related to the physical work environment, but also age and occupational characteristics (occupation and economic activity of the company). Our results on the associations between covariates (age, occupation, physical– chemical–biological–biomechanical exposures) and sickness absence were consistent with the literature [Prins and de Graaf, 1986; Messing et al., 1998; Boedeker, 2001; Fuhrer et al., 2002] and confirmed the validity of our results. Additional results exploring the differences in the associations between psychosocial work factors and sickness absence according to occupational groups suggested that
there may be very few differences in these associations. Consequently, most of the associations between psychosocial work factors and sickness absence spells and days may be similar for all occupational groups. This study also had some limitations. The study design was cross-sectional, consequently no causal conclusion could be drawn from the results. Furthermore, a reverse causation may be suspected, as sickness absence may also have an impact on psychosocial work environment. No information was available on the number of absence days for each absence spell, making the analysis of short or long spells impossible. Moreover, the information about sickness absence was based on selfadministered questionnaires, but studies reported a high agreement between self-reported sickness absence and information from official registers [Voss et al., 2008]. Furthermore, psychosocial work factors were not all based on validated questionnaires, but other studies underlined the validity and interest of using proxies [Karasek et al., 2007]. Some psychosocial work factors may have not been taken into account, like discrimination, role conflicts, quality of leadership or organizational justice, that were found as risk factors for sickness absence in other studies [Kivimaki et al., 2003a; Rugulies et al., 2007; Nyberg et al., 2008; Roelen et al., 2009]. In our study, we had no information about the presence of chronic diseases, so we were unable to test its potential impact on the association between psychosocial work factors and sickness absence. A reporting bias may be possible as both psychosocial work factors and sickness absence were self-reported; this might lead to an overestimation of the associations observed. Finally, a healthy worker effect may also be suspected (unhealthy employees may have changed to less exposed jobs or left the market), leading to an underestimation of the associations between exposures and sickness absence.
Sensitivity Analyses Additional analyses were performed with employment status (permanent vs. temporary position) as an additional covariate and the results were found to be unchanged. Additional analyses were also performed taking sample design and weights into account and provided similar results
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TABLE II. Associations Between Psychosocial Work Factors and Sickness Absence Spells for Men and Women (Each Psychosocial Work Factor Studied Separately) Men OR (95% CI) Low-skill discretion 1absence vs. 0 2 vs. 0 3 vs. 0 Low-decision authority 1vs. 0 2 vs. 0 3 vs. 0 Low-decision latitude 1vs. 0 2 vs. 0 3 vs. 0 High-psychological demands 1vs. 0 2 vs. 0 3 vs. 0 Low-social support (supervisors) 1vs. 0 2 vs. 0 3 vs. 0 Low-social support (colleagues) 1vs. 0 2 vs. 0 3 vs. 0 Low-social support 1vs. 0 2 vs. 0 3 vs. 0 Job strain 1vs. 0 2 vs. 0 3 vs. 0 Iso-strain 1vs. 0 2 vs. 0 3 vs. 0 Low esteem 1vs. 0 2 vs. 0 3 vs. 0 Job insecurity 1vs. 0 2 vs. 0 3 vs. 0 Low-job promotion 1vs. 0 2 vs. 0 3 vs. 0
Women OR (95% CI)
1.27 (1.19^1.35) 1.47 (1.32 -1.64) 2.12 (1.78^2.52)
1.18 (1.11^1.26) 1.56 (1.39^1.74) 1.86 (1.55^2.24)
1.26 (1.18^1.34) 1.64 (1.47^1.82) 2.26 (1.90^2.68)
1.17 (1.10^1.24) 1.42 (1.28^1.57) 1.66 (1.42^1.95)
1.30 (1.23^1.38) 1.62 (1.46^1.79) 2.49 (2.11^2.92)
NS
1.10 (1.03^1.17) 1.09 (0.98^1.21) 1.22 (1.03^1.45)
NS
NS NS
1.24 (1.15^1.33) 1.61 (1.43^1.80) 1.97 (1.67^2.32)
NS
NS
1.28 (1.20^1.37) 1.45 (1.31^1.61) 2.18 (1.88^2.52) 1.34 (1.25^1.43) 1.69 (1.52^1.88) 2.22 (1.92^2.57) 1.21 (1.12^1.30) 1.35 (1.20^1.51) 1.33 (1.14^1.55)
NS NS
1.34 (1.25^1.44) 1.81 (1.62^2.02) 2.38 (2.03^2.81)
1.37 (1.28^1.46) 1.72 (1.55^1.92) 2.35 (2.03^2.72)
1.38 (1.28^1.49) 1.66 (1.49^1.85) 2.47 (2.14^2.85)
1.49 (1.36^1.63) 1.80 (1.59^2.05) 2.74 (2.34^3.21)
1.40 (1.32^1.49) 1.87 (1.68^2.07) 3.47 (2.91^4.14)
1.29 (1.21^1.37) 1.80 (1.62^1.99) 2.33 (1.98^2.75)
1.50 (1.40^1.60) 1.89 (1.70^2.10) 2.64 (2.28^3.06)
1.41 (1.33^2.19) 1.98 (1.79^2.19) 3.50 (2.95^4.16)
1.21 (1.12^1.30) 1.56 (1.38^1.76) 1.75 (1.47^2.08)
1.27 (1.20^1.35) 1.58 (1.42^1.75) 2.40 (2.03^2.83)
1.44 (1.32^1.56) 2.01 (1.77^2.28) 2.64 (2.20^3.16)
1.20 (1.12^1.29) 1.45 (1.30^1.62) 1.69 (1.44^1.98)
1.28 (1.20^1.38) 1.54 (1.38^1.70) 2.37 (2.04^2.74)
1.45 (1.36^1.56) 1.76 (1.58^1.95) 2.81 (2.42^3.26)
(Continued )
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TABLE II. (Continued.) Men OR (95% CI) Low reward 1vs. 0 2 vs. 0 3 vs. 0 Long working hours 1vs. 0 2 vs. 0 3 vs. 0 Night work 1vs. 0 2 vs. 0 3 vs. 0 Shift work 1vs. 0 2 vs. 0 3 vs. 0 Asocial workdays 1vs. 0 2 vs. 0 3 vs. 0 Low predictability 1vs. 0 2 vs. 0 3 vs. 0 Physical violence/sexual assault 1vs. 0 2 vs. 0 3 vs. 0 Bullying 1vs. 0 2 vs. 0 3 vs. 0 Verbal abuse 1vs. 0 2 vs. 0 3 vs. 0 Demands for responsibility 1vs. 0 2 vs. 0 3 vs. 0
Women OR (95% CI)
1.43 (1.35^1.52) 2.04 (1.83^2.26) 4.28 (3.52^5.18)
0.75 (0.67^0.84) 0.50 (0.40^0.64) 0.33 (0.20^0.53) NS 0.96 (0.87^1.07) 0.86 (0.72^1.03) 0.93 (0.72^1.22)
NS
NS NS
1.37 (1.08^1.73) 1.93 (1.40^2.68) 2.42 (1.56^3.74)
NS NS
NS NS
NS NS
0.75 (0.62^0.91) 0.78 (0.57^1.06) 0.52 (0.30^0.89) NS 1.04 (0.86^1.28) 0.86 (0.62^1.19) 1.08 (0.72^1.61) 1.24 (1.12^1.36) 1.29 (1.12^1.49) 1.38 (1.15^1.67) NS 1.05 (0.96^1.15) 0.99 (0.86^1.14) 1.11 (0.93^1.34) NS 1.00 (0.93^1.08) 1.12 (1.00^1.26) 1.05 (0.90^1.22)
NS NS
1.31 (1.22^1.40) 1.98 (1.78^2.20) 3.15 (2.69^3.70)
1.43 (1.33^1.54) 1.99 (1.78^2.23) 2.46 (2.08^2.92)
NS NS
NS NS
NS NS
NS NS
NS NS
1.73 (1.36^2.18) 1.87 (1.34^2.61) 2.42 (1.61^3.63)
NS NS
1.39 (1.29^1.50) 1.93 (1.73^2.16) 2.89 (2.50^3.34)
1.40 (1.30^1.51) 1.98 (1.78^2.21) 2.48 (2.14^2.87)
1.07 (1.00^1.15) 1.06 (0.95^1.18) 1.27 (1.09^1.47)
NS
NS 1.00 (0.94^1.07) 0.98 (0.88^1.08) 1.07 (0.90^1.26)
0.92 (0.86^0.98) 0.94 (0.84^1.04) 0.83 (0.70^0.98)
1.21 (1.12^1.30) 1.18 (1.05^1.34) 1.42 (1.18^1.70) NS 0.96 (0.90^1.04) 0.87 (0.77^0.99) 0.86 (0.71^1.04)
1.50 (1.41^1.61) 1.89 (1.70^2.10) 3.17 (2.70^3.72)
NS NS
Generalizedlogitregression analysis adjustedforage,occupation,economic activity,physical,chemical,biological, andbiomechanical exposures.Foreach variable,thefirst P-value provides the result of the global test,the second P-value is testing the difference between the first and the second category, and finally the third P-value is testing the difference between the second and the third category. NS, not significant. Bold: OR significant at 5%. P < 0.05. P < 0.01. P < 0.001 (chi-square and student test for the comparison between men and women).
for sickness absence spells, suggesting that our results may be generalized to the whole French population of employees (Supplementary Tables SI and SIII). Regarding sickness absence days, the results were different as the number of
significant MRs was strongly reduced from Table III to Supplementary Table SII and the MRs significant in Table V were no longer significant in the additional analyses (Supplementary Table SIV). This may be explained by two
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TABLE III. Associations Between Psychosocial Work Factors and Sickness Absence Days for Men and Women (Each Psychosocial Work Factor Studied Separately) Men MR Low-skill discretion Low-decision authority Low-decision latitude High-psychological demands Low-social support (Supervisors) Low-social support (Colleagues) Low-social support Job strain Iso-strain Low esteem Job insecurity Low-job promotion Low reward Long working hours Night work Shift work Asocial workdays Low predictability Physical violence/sexual assault Bullying Verbal abuse Demands for responsibility
1.09 1.09 1.09 1.09 1.15 1.03 1.22 1.13 1.16 1.22 1.10 1.16 1.22 1.20 1.00 1.06 1.05 1.00 1.37 1.18 1.10 1.04
Women 95% CI
(1.03^1.15) (1.03^1.15) (1.03^1.14) (1.03^1.14) (1.09^1.21) (0.98^1.09) (1.16^1.28) (1.06^1.19) (1.09^1.24) (1.16^1.29) (1.04^1.16) (1.10^1.22) (1.16^1.29) (1.08^1.33) (0.91^1.10) (0.99^1.12) (0.99^1.12) (0.95^1.06) (1.14^1.64) (1.11^1.25) (1.04^1.17) (0.98^1.09)
MR
95% CI
1.09 0.98 1.02 1.12 1.16 1.03 1.14 1.10 1.10 1.12 1.15 1.18 1.12 0.98 0.90 1.01 1.06 1.04 1.29 1.14 1.12 1.03
(1.03^1.15) (0.92^1.05) (0.97^1.09) (1.06^1.19) (1.10^1.24) (0.97^1.09) (1.07^1.20) (1.04^1.17) (1.03^1.17) (1.06^1.19) (1.08^1.21) (1.12^1.24) (1.06^1.18) (0.83^1.15) (0.77^1.06) (0.94^1.09) (0.98^1.14) (0.98^1.11) (1.09^1.53) (1.07^1.21) (1.06^1.19) (0.97^1.09)
Negative binomial regression analysis adjusted for age, occupation, economic activity, physical, chemical, biological, and biomechanical exposures. Bold: MR significant at 5%. P < 0.01. P < 0.001 (chi-square and student test for the comparison between men and women).
phenomena: (1) the MRs were very low even if significant in Table V; and (2) taking sample design and weights into account reduced the precision of the estimates and made them non-significant. Consequently, the results for sickness absence days should be taken with caution, as the associations may not be significant.
Comparison With the Literature Our results concerning the three Karasek’s job stress dimensions confirm the previous findings from the literature. In fact, low-decision latitude, high-psychological demands, and low-social support have already been observed as risk factors of sickness absence in other studies [Gimeno et al., 2004; Moreau et al., 2004; Niedhammer et al., 2008]. The two sub-dimensions of decision latitude, low skill discretion and low-decision authority, were also found to be risk factors for sickness absence for both genders [Väänänen et al., 2003]. Low reward was observed as a risk factor of sickness absence. Two other studies, among nurses’ aides and Danish
employees also reported the association of reward with sickness absence [Eriksen et al., 2003; Lund et al., 2005]. In agreement with one previous study [Niedhammer et al., 2013], the sub-dimensions of job insecurity and low-job promotion were risk factors for sickness absence. Low esteem was also found to be a risk factor in our study but to our knowledge, no previous study found an association between esteem and sickness absence. The following five working time variables were included in our study: long working hours, night work, shift work, asocial work days, and predictability. Long working hours was a protective factor of sickness absence spells. This result was also reported in the literature [Voss et al., 2001]. An explanation could be that long working hours may be associated with a high pressure at work and difficulty for workers to take sickness absence. However, we also found that long working hours increased the number of sickness absence days. Consequently, employees working more than 48 hr per week were less likely to take sickness absence, but if absent, they were more likely to take a longer duration of absence. Night work and asocial work days were
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TABLE IV. Associations Between Psychosocial Work Factors and Sickness Absence Spells for Men and Women (All Psychosocial Work Factors Studied Simultaneously) Men (N ¼ 24,464)
Women (N ¼18,110)
OR (95% CI) Low-decision latitude 1absence vs. 0 2 vs. 0 3 vs. 0 High-psychological demands 1vs. 0 2 vs. 0 3 vs. 0 Low-social support 1vs. 0 2 vs. 0 3 vs. 0 Low reward 1vs. 0 2 vs. 0 3 vs. 0 Long working hours 1vs. 0 2 vs. 0 3 vs. 0 Shift work 1vs. 0 2 vs. 0 3 vs. 0 Low predictability 1vs. 0 2 vs. 0 3 vs. 0 Physical violence/sexual assault 1vs. 0 2 vs. 0 3 vs. 0 Bullying 1vs. 0 2 vs. 0 3 vs. 0 Verbal abuse 1vs. 0 2 vs. 0 3 vs. 0 Demands for responsibility 1vs. 0 2 vs. 0 3 vs. 0
OR (95% CI)
1.13 (1.06^1.21) 1.37 (1.22^1.53) 1.68 (1.39^2.03) 1.04 (0.97^1.12) 1.07 (0.96^1.20) 1.02 (0.86^1.22) 1.06 (0.98^1.13) 1.05 (0.93^1.18) 1.15 (0.96^1.39)
NS NS NS NS NS NS NS
1.28 (1.19^1.38) 1.63 (1.44^1.84) 2.91 (2.33^3.64) 0.74 (0.66^0.84) 0.50 (0.39^0.64) 0.35 (0.20^0.58) 1.16 (1.07^1.26) 1.13 (0.99^1.29) 1.31 (1.07^1.59) 0.88 (0.82^0.94) 0.88 (0.79^0.99) 0.74 (0.62^0.89) 1.08 (0.84^1.39) 1.13 (0.78^1.62) 1.25 (0.76^2.04)
NS
NS NS
NS NS NS NS NS
1.14 (1.05^1.23) 1.34 (1.19^1.52) 1.59 (1.33^1.90) 1.11 (1.03^1.20) 1.12 (0.99^1.26) 1.42 (1.20^1.67) 1.11 (1.03^1.21) 1.16 (1.03^1.32) 1.22 (1.02^1.45) 1.31 (1.21^1.42) 1.40 (1.24^1.59) 1.90 (1.57^2.28) 0.73 (0.60^0.89) 0.76 (0.55^1.06) 0.50 (0.28^0.88) 1.19 (1.08^1.32) 1.16 (0.99^1.15) 1.30 (1.06^1.60) 0.94 (0.87^1.02) 1.02 (0.90^1.15) 0.85 (0.72^1.01)
1.26 (1.16^1.37) 1.56 (1.36^1.76) 1.51 (1.25^1.84)
0.96 (0.90^1.02) 0.92 (0.82^1.02) 0.94 (0.79^1.12)
NS NS NS NS
NS
NS
NS NS
NS
NS NS
NS NS NS NS NS
1.45 (1.13^1.86) 1.19 (0.83^1.70) 1.30 (0.84^2.03)
1.10 (1.01^1.19) 1.44 (1.27^1.63) 1.92 (1.59^2.30)
NS NS
1.12 (1.03^1.23) 1.40 (1.24^1.60) 1.78 (1.50^2.10) 1.20 (1.10^1.30) 1.55 (1.37^1.75) 1.59 (1.35^1.88) 1.01 (0.93^1.09) 0.96 (0.85^1.08) 1.06 (0.90^1.24)
NS NS NS NS
Generalizedlogitregression analysis adjustedforage,occupation,economic activity,physical,chemical,biological, andbiomechanical exposures.Foreach variable,thefirst P-value provides the result of the global test,the second P-value is testing the difference between the first and the second category, and finally the third P-value is testing the difference between the second and the third category. NS, not significant. Bold: OR significant at 5%. P < 0.05. P < 0.01. P < 0.001 (chi-square and student test for the comparison between men and women).
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TABLE V. Associations Between Psychosocial Work Factors and Sickness Absence Days for Men and Women (All Psychosocial Work Factors Studied Simultaneously) Men (N ¼ 7,191)
Low-decision latitude High-psychological demands Low-social support Low reward Long working hours Shift work Low predictability Physical violence/sexual assault Bullying Verbal abuse Demands for responsibility
Women (N ¼ 6,416)
MR
95% CI
MR
95% CI
1.02 0.99 1.15 1.08 1.20 1.01 0.98 1.31 1.11 0.99 1.01
(0.96^1.08) (0.93^1.05) (1.08^1.22) (1.02^1.15) (1.07^1.34) (0.94^1.08) (0.92^1.04) (1.08^1.59) (1.04^1.18) (0.93^1.06) (0.96^1.07)
0.99 1.06 1.09 1.04 1.00 0.90 1.01 1.16 1.08 1.03 1.00
(0.93^1.05) (1.00^1.13) (1.02^1.16) (0.97^1.11) (0.84^1.19) (0.91^1.07) (0.95^1.08) (0.97^1.38) (1.01^1.16) (0.96^1.10) (0.94^1.06)
Negative binomial regression analysis adjusted for age, occupation, economic activity, physical, chemical, biological, and biomechanical exposures. Bold: MR significant at 5%. P < 0.05. P < 0.01. P < 0.001 (chi-square and student test for the comparison between men and women).
not found to be associated with absence spells or days. Concerning night work, another study found no significant association between night work and sickness absence [Niedhammer et al., 2013]. Regarding shift work, an association was found with sickness absence spells for men but the rare previous studies provided inconclusive results [Kleiven et al., 1998; Tüchsen et al., 2008; Böckerman and Laukkanen, 2010; Niedhammer et al., 2013]. Finally, low predictability was a protective factor for sickness absence spells among men, something quite unexpected but to our knowledge, no previous study investigated the association between predictability of schedules and sickness absence. For the three factors related to workplace violence, bullying and physical violence/sexual assault were observed as risk factors of sickness absence in our study, confirming the findings from previous studies [Kivimaki et al., 2000; Vingård et al., 2005; Rugulies et al., 2007]. Verbal abuse also increased the risk of sickness absence in our study but to our knowledge, no previous study studied the association of verbal abuse with sickness absence. Verbal abuse may be close to bullying. Indeed, some items of bullying may also be possible situations of verbal abuse. The two factors were found to be associated with sickness absence spells, but bullying was observed as a risk factor of sickness absence days but not verbal abuse. Although associated, these two factors might thus measure different aspects of workplace violence. Demands for responsibility were not associated with sickness absence spells or days and to our knowledge, there have been no previous studies exploring this factor.
In the final model with all psychosocial work factors included simultaneously (Models II); the significant associations were independent of the other psychosocial work factors taken into account. The associations between psychosocial work factors are not necessarily well known and some factors may be causes or consequences of other factors [Rugulies et al., 2007, 2010]. This is why studying each factor separately (Models I) without adjusting for all factors together may also be interesting. The significant associations found in Models II were also observed when studying each factor separately, but some additional associations were found: high-psychological demands, low-social support, and physical violence/sexual assault were found to be risk factors for sickness absence spells among men and low-decision latitude, high-psychological demands for men, low reward, physical violence/sexual assault for women, and verbal abuse for both genders were found to be risk factors for sickness absence days. In our study, a lower number of significant associations was found with sickness absence days; indeed, some factors were associated with absence spells but not with the duration of sickness absence. This result can be explained by the fact that the duration of sickness absence was studied among those who had at least one absence day, so this analysis was based on a sub-sample while the study of sickness absence spells was based on the whole sample. Such analyses made possible to disentangle the factors that may have an association with sickness absence spells and those that may be associated with absence days. A previous study using an European sample found similar results [Niedhammer et al., 2013].
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CONCLUSION Our findings illustrate the importance of psychosocial work factors as risk factors of sickness absence, but their role may be more limited in the duration of absence. This study adds to the body of evidence that psychosocial work environment, not only the well-known factors but also more recent factors, should be a target for the prevention of sickness absence and the improvement of health at work.
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SUPPORTING INFORMATION
Van der Hulst M. 2003. Long workhours and health. Scand J Work Environ Health 29:171–188.
Additional supporting information may be found in the online version of this article at the publisher’s web-site.
Psychosocial Factors at Work and Sickness Absence: Results from the French National SUMER Survey Thomas Lesuffleur,
1,2 MSc,
Jean-François Chastang, PhD,1,2 Nicolas Sandret, and Isabelle Niedhammer, PhD1,2
MD,
3
Objective This study aims at exploring the associations between psychosocial work factors and sickness absence. Methods The sample from the French National Survey SUMER 2010 included 46,962 employees. Sickness absence spells and days within the last year were studied as two outcomes. Psychosocial work factors included psychological demands, decision latitude, social support, reward, working time, and workplace violence variables. Covariates were age, occupation, economic activity, and other occupational exposures. Results For both genders, low latitude, low reward, shift work, bullying, and verbal abuse were risk factors of absence spells while long working hours were a protective factor. High demands, low support, and physical violence were risk factors for women. Low support and bullying for both genders, high demands for women, and low reward, long working hours, and physical violence for men increased absence duration. Conclusions This study highlights the importance of psychosocial work factors as risk factors of sickness absence. Am. J. Ind. Med. 57:695–708, 2014. ß 2014 Wiley Periodicals, Inc. KEY WORDS: psychosocial work factors; job stress; workplace violence, sickness absence; France
INTRODUCTION Sickness absence is recognized as a global indicator of health status, and as a marker of social, psychological, and physical functioning for working populations [Marmot et al., 1995; Kivimaki et al., 2003b]. The occurrence of sickness
1 INSERM,UMR_S1136,Pierre Louis Institute of Epidemiologyand PublicHealth,Department of Social Epidemiology, F-75013, Paris, France 2 Sorbonne Universite´s, UPMC Univ Paris 06, UMR_S1136, Pierre Louis Institute of Epidemiology and Public Health, Department of Social Epidemiology, F-75013, Paris, France 3 Inspection Me´dicale duTravail, Paris, France Contract grant sponsor: French Ministry of Labour (DARES); Contract grant number: 2200684049. Disclosure Statement: The authors report no conflicts of interests. Correspondence to: Dr. Isabelle Niedhammer, PhD, INSERM UMRS 1136çIPLESP, Team 7 (ERES), Faculte´ de Me´decine Pierre et Marie Curieçp^ole Saint-Antoine, 27 rue de Chaligny, 75012 Paris, France. E-mail: [email protected]
Accepted13 February 2014 DOI10.1002/ajim.22317. Published online17 March 2014 in Wiley Online Library (wileyonlinelibrary.com).
ß 2014 Wiley Periodicals, Inc.
absence may be influenced by various factors, such as social, personal, and demographic characteristics, exposures from work environment and conditions specific to a given workplace or organization [Prins and de Graaf, 1986; Alexanderson, 1998; Niedhammer et al., 1998]. Because sickness absence is strongly related to health and has an important economic impact in terms of costs for health insurance and loss of productivity, identification of the factors associated with this outcome becomes essential as a guide to preventive measures. Work-related factors and especially psychosocial work factors have an important impact on worker’s health and may play a role in the occurrence of sickness absence. To evaluate psychosocial work factors, theoretical models have appeared in the literature within the last decades. The job strain model, developed by Karasek et al. [1998], is probably the most used theoretical model of job stress and is composed of three main dimensions, psychological demands, decision latitude including two subdimensions, skill discretion and decision authority, and
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social support at work from colleagues and supervisors. Job strain defined by the combination of high demands and low latitude is considered to be the most detrimental situation, as well as iso-strain, the combination of job strain and low support. Another model, the effort–reward imbalance model focuses on effort and reward at work in terms of esteem, job promotion, and job security [Siegrist et al., 2004]. Other concepts have emerged more recently: those related to workplace violence, such as physical violence [Loomis et al., 2001], sexual harassment [Sbraga and O’Donohue, 2000], verbal abuse and bullying [Einarsen, 2000], working hours/ time such as long working hours [Van der Hulst, 2003], predictability [Vaananen et al., 2008], shift work, night work, and asocial work days [Merkus et al., 2012], as well as demands for responsibility [Pejtersen et al., 2010]. Studies reported an increased risk of sickness absence among workers exposed to high levels of psychological demands, low-decision latitude, and low-social support [Niedhammer et al., 1998; Gimeno et al., 2004; Moreau et al., 2004]. For other recent concepts, the literature remains sparse. Factors like low-job promotion, long working hours, shift work, or bullying were found to be associated with sickness absence [Kivimaki et al., 2000; Voss et al., 2001; Merkus et al., 2012; Hinkka et al., 2013; Niedhammer et al., 2013]. Furthermore, only few studies explored a large set of psychosocial work factors, and when it was the case, these studies focused on specific working populations (i.e., specific occupations or work sectors) [Eriksen et al., 2003; Lund et al., 2005; Roelen et al., 2009]. Moreover, most of these studies did not always take important covariates into account, especially those related to the physical working environment. In the present study, we measured sickness absence by the number of sickness absence spells and also by the number of absence days in order to study the differences in the associations of psychosocial work conditions with sickness absence spells and days. This study was based on a large French database, examined various measures of psychosocial work factors following well-known theoretical models and emergent concepts, and also included occupational exposures of physical, chemical, biomechanical, and biological nature. The objectives of the study were to explore the associations between a large number of psychosocial work factors and sickness absence (both spells and days) in a large national representative sample of French employees.
METHODS Study Population The SUMER survey is a periodical national survey from the DARES (French Ministry of Labour) conducted every 7 years. Its objective is to evaluate all kinds of occupational exposures among the national working population of
employees, in order to define preventive strategies and research priorities in France. SUMER 2010, the last survey conducted in 2010, included around 50,000 employees interviewed about their physical, biological, chemical, biomechanical, and organizational/psychosocial exposures by 2,400 occupational physicians. Employees also completed a self-administered questionnaire in which their responses were collected about the job strain and reward questionnaires, as well as about other psychosocial work factors such as workplace violence. The SUMER survey was approved by the French Ethics Committees called Commission Nationale de l’Informatique et des Libertés—CNIL- and Conseil National de l’Information Statistique—CNIS (informed consent was not necessary for this survey).
Sickness Absence Data Sickness absence was provided by the following two items: the number of spells of absence (0, 1, 2, or 3 and more) for health-related reasons (excluding work accident or maternity) within the last 12 months and the total number of absence days within the same period of time. Consequently, two variables were used: a variable with four categories representing the number of sickness absence spells within the past 12 months and a variable representing the number of sickness absence days for the employees with at least one absence spell within the last 12 months.
Psychosocial Factors at Work Twenty-two psychosocial work factors were constructed. Karasek’s job stress dimensions were constructed using the validated French version of the questionnaire [Niedhammer, 2002; Niedhammer et al., 2006]: decision latitude (nine items, Cronbach alpha ¼ 0.78, including six items for skill discretion, three items for decision authority), psychological demands (nine items, Cronbach alpha ¼ 0.80) and social support (eight items, Cronbach alpha ¼ 0.82, including four items for social support from colleagues and four items for social support from supervisors). The scores were constructed according to the recommendations by Karasek and dichotomized at the median of the total sample [Karasek et al., 1998]. Job strain was defined by the combination of high demands and low latitude, and iso-strain by the combination of job strain and low support. Reward (11 items, Cronbach alpha ¼ 0.85, including 5 items for esteem, 2 items for job security and 4 items for job promotion) from the effort–reward imbalance model was measured using the validated French version of this questionnaire [Niedhammer et al., 2000]. Reward and its sub-dimensions were dichotomized at the median of the total sample.
Psychosocial Factors at Work and Sickness Absence
The following five working time variables were studied: long working hours (one item, 48 hr/week following the European Directive on working time), night work (one item, working between 12 and 5 a.m. 1 night/week), shift work (one item, either permanent or alternating/rotating shifts), asocial work days (one item, working on Sundays or Saturdays once/week) and predictability of schedules (four items, information about time schedules for the next day, week, month, and the next 3 months). Shift and night work were included in this group of factors; they are recognized as chronobiological risk factors for a number of health outcomes but they may also been seen as psychosocial work factors as they are strongly related to work organization and may also disturb social and family life. Three factors were related to workplace violence: physical violence or sexual assault (two items), bullying (one item), and verbal abuse (two items). Exposure was defined by at least one situation of workplace violence for each factor, when the measure was based on more than one item. Demands for responsibility (four items: a mistake in work may lead to serious consequences for product/service quality, to serious financial losses for the company, dangerous consequences for the safety of people or yourself, and to wage/work/job sanctions for yourself) were dichotomized at the median of the total sample.
Covariates Several covariates, age, occupation coded using the French classification of occupation (PCS-INSEE) which is close to the International Standard Classification of Occupation (ISCO), economic activity of the company coded using the European classification (NACE) and four other occupational exposures (physical, biomechanical, biological, and chemical exposures) were included. Physical exposure was defined by at least 20 hr of exposure to noise (impulses or other disturbing noises), thermic constraints (or humidity) or radiation, within the previous week. Biomechanical exposure was defined by at least 20 hr of exposure to manual materials handling, postural and articular constraints, vibrations (manual handling of vibrating tools and vibration from a fixed machine) or driving (driving of specialized machinery, car, bus) within the previous week. Biological exposure was defined by at least one biological exposure within the previous week. Chemical exposure was defined by at least one chemical exposure within the previous week. These covariates were chosen because they were found to be associated with sickness absence in the literature [Prins and de Graaf, 1986; Messing et al., 1998; Boedeker, 2001; Fuhrer et al., 2002] and may also be associated with psychosocial work factors. Consequently, adjusting for these
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covariates may be useful in the study of the associations between psychosocial work factors and sickness absence.
Statistical Methods The description of the sample was provided and the differences between genders for all variables were tested using the chi-square and the Student’s t-test. As the variable representing the number of sickness absence spells contains four categories, it was studied using generalized logit regression model that allowed to study the risk of having at least one absence spell but also the risk associated with the increase in the number of sickness absence spells. This model produces odds ratios (ORs), that is, odds of having one (respectively two, three, or more) sickness absence spell associated with each independent variable. The number of sickness absence days is a form of count data, and the association between psychosocial work factors and sickness absence days was studied among the subsample of employees who had at least 1 day of absence using negative binomial regression model. This model produces mean ratios (MRs) for the number of absence days associated with each independent variable. Full details about these models may be found elsewhere [Niedhammer et al., 2013]. The two outcomes were thus used to perform two different analyses, a first analysis on the whole sample to identify the factors associated with the presence of sickness absence spells, and a second analysis on the sub-sample of those with at least one absence day to identify the factors associated with the duration of absence. First, all psychosocial work factors were studied separately with adjustment for covariates (models I). Then, all the psychosocial work factors significant for at least one gender were included simultaneously as independent variables in a final model with adjustment for covariates (models II). Although associations were found between psychosocial work factors, no colinearity was detected in models II. An interaction term between decision latitude and psychological demands was tested following Karasek’s hypothesis, but no significant interaction was found. Men and women were analyzed separately, and all analyses were performed using SAS.
RESULTS Description The sample included 46,962 employees, 26,883 men and 20,079 women. The response rate was 87%. Significant differences were observed between genders for age, occupation, economic activity, skill discretion, decision authority, decision latitude, psychological demands, job strain, isostrain, esteem, insecurity, long working hours, night work, shift work, predictability, physical violence/sexual assault,
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bullying, verbal abuse, demands for responsibility, and other occupational exposures. The description of the sample is shown in Table I, according to gender for all studied variables. The rate of absence spells was significantly higher for women than for men. There was no significant difference in the mean of sickness absence days: 16.6 days for men (standard deviation: 32.9) and 17.5 days for women (standard deviation: 34.2) among the sample of those having at least one absence day within the last year.
Generalized Logit and Negative Binomial Models (Models I) Tables II and III provide the results of the associations between psychosocial work factors and sickness absence, each factor being studied separately with adjustment for covariates. For both genders, almost all psychosocial work factors were significantly associated with sickness absence spells except night work and asocial work hours for both genders, demands for responsibility for men and predictability for women (Table II). Among all these psychosocial work factors, only long working hours for both genders and low predictability for men were protective factors. For most of these associations (Table II), the association was stronger with the increase of the number of absence spells. For example, for men, the ORs associated with bullying was 1.31 for one sickness absence spell, 1.98 for two sickness absence spells and 3.15 for three or more sickness absence spells. The risk increased significantly from one category to another. Concerning the number of sickness absence days among the sub-sample of employees with sickness absence (Table III), the number of significant associations was lower: skill discretion, psychological demands, social support from colleagues, social support, job strain, iso-strain, esteem, insecurity, job promotion, reward, physical violence/sexual assault, bullying, and verbal abuse for both genders and decision authority, decision latitude and long working hours for men were associated with sickness absence days. The strongest association was found for physical violence/sexual assault for both genders: men exposed to physical violence/sexual assault had 37% more absence days than men who were not exposed, and women exposed to physical violence/sexual assault had 29% more absence days than non-exposed women. Night work and asocial work hours were the only two variables that were not associated with sickness absence (both spells and days) for both genders. Concerning the associations between covariates and sickness absence spells, all covariates were significant except chemical exposure for men and biological exposure for
women. Sickness absence spells were significantly higher for low-skilled occupations and younger employees. In addition, regarding the number of sickness absence days, older employees were more likely to have longer absences than younger employees (not shown).
Generalized Logit and Negative Binomial Models (Models II) Tables IV and V show the associations of all psychosocial work factors included simultaneously in the same model with adjustment for covariates. The factors associated with sickness absence spells (Table IV) were low-decision latitude, low reward, shift work, bullying, and verbal abuse for both genders and highpsychological demands, low-social support and physical violence/sexual assault for women. Long working hours were a protective factor of sickness absence spells for both genders and low predictability was a protective factor for men. Regarding the results for the sub-dimensions of decision latitude and reward, low skill discretion, low-decision authority, low esteem, job insecurity, and low-job promotion were risk factors of sickness absence spells for both genders (not shown). Among the sub-sample of employees with sickness absence, the number of sickness absence days (Table V) increased with low-social support and bullying for both genders, low reward, long working hours, and physical violence/sexual assault for men and high-psychological demands for women. The strongest association was found for physical violence/sexual assault (MR ¼ 1.31) for men. Regarding covariates, being younger, low-skilled occupations, physical, biomechanical (except for women), and biological exposure (except for women) were found to be risk factors of sickness absence spells. For the duration of sickness absence, being older, low-skilled occupations, biomechanical exposure for men, and physical exposure for women were found to be risk factors (not shown).
DISCUSSION Main Results In this study, we analyzed the associations of twenty-two psychosocial work factors with sickness absence spells and days among both genders. The multivariate analysis, including all psychosocial work factors together with adjustment for covariates, showed that low-decision latitude, low reward, shift work, bullying, and verbal abuse were risk factors for sickness absence spells while long working hours was a protective factor for both genders. High-psychological demands, low-social support, and physical violence/sexual assault for women were risk factors of sickness absence
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TABLE I. Study Population, French SUMER National Survey, 2010 Men (N ¼ 26,883), N (%) Age (years) 48 hr/week) Night work Non-exposed Exposed Shift work Non-exposed Exposed Asocial workdays Non-exposed Exposed Predictability High Low Physical violence/sexual assault Non-exposed Exposed Bullying Non-exposed Exposed Verbal abuse Non-exposed Exposed Demands for responsibility Low High Chemical exposure Non-exposed Exposed Biological exposure Non-exposed Exposed Physical exposure Non-exposed Exposed Biomechanical exposure Non-exposed Exposed Sickness absence spells 0 1 2
Men (N ¼ 26,883), N (%)
Women (N ¼ 20,079), N (%)
15,161 (57.0) 11,439 (43.0)
11,063 (56.2) 8,623 (43.8)
13,002 (48.9) 13,603 (51.1)
9,634 (48.8) 10,117 (51.2)
P-value NS
NS
24,097 (90.2) 2,614 (9.8)
19,195 (96.3) 734 (3.7)
24,203 (91.6) 2,214 (8.4)
19,356 (97.2) 558 (2.8)
21,261 (79.5) 5,492 (20.5)
16,805 (84.1) 3,189 (15.9)
21,178 (78.8) 5,680 (21.2)
16,564 (82.5) 3,503 (17.5)
18,049 (67.3) 8,766 (32.7)
14,009 (69.9) 6,017 (30.1)
25,813 (98.5) 402 (1.5)
19,112 (98.1) 375 (1.9)
20,881 (77.7) 6,002 (22.3)
15,324 (76.3) 4,755 (23.7)
21,208 (80.2) 5,224 (19.8)
14,362 (72.8) 5,375 (27.2)
11,388 (42.4) 15,574 (57.6)
13,213 (65.9) 6,843 (34.1)
16,597 (61.7) 10,286 (38.3)
14,706 (73.2) 5,373 (26.8)
22,601 (84.1) 4,282 (15.9)
14,120 (70.3) 5,959 (29.7)
20,633 (76.7) 6,250 (23.3)
17,881 (89.1) 2,198 (10.9)
12,566 (46.7) 14,317 (53.3)
11,471 (57.1) 8,608 (42.9)
18,133 (68.3) 6,016 (22.7) 1,722 (6.5)
12,319 (62.0) 5,009 (25.2) 1,685 (8.5) (Continued )
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TABLE I. (Continued.)
3 Sickness absence days
Men (N ¼ 26,883), N (%)
Women (N ¼ 20,079), N (%)
P-value
667 (2.5) Men (N ¼ 7,768), mean (SD) 16.6 (32.9)
844 (4.2) Women (N ¼ 6,978), mean (SD) 17.5 (34.2)
NS
NS, not significant. P < 0.05. P < 0.01. P < 0.001 (chi-square and student test for the comparison between men and women).
spells. Regarding the number of sickness absence days among the sub-sample of employees with sickness absence, male and female employees exposed to low-social support and bullying were more likely to have longer absence. In addition, low reward, long working hours, and physical violence/sexual assault among men and high-psychological demands among women increased the number of absence days. Other results from less conservative models (including each factor separately and not together) provided a higher number of factors associated with sickness absence spells and days.
Strengths and Limitations of the Study The study was based on a large national representative sample of French employees, and the response rate was very satisfactory (87%), making a participation bias unlikely. The large sample size allowed to perform separate analysis for men and women. The study included a large variety of psychosocial work factors, with both classical and emergent factors. The validated French version of the questionnaires for measuring psychological demands, decision latitude, social support, and reward were used. Furthermore, we used two measures of sickness absence, spells and days of sickness absence, making the study of the differences in the associations between psychosocial work conditions and these two outcomes possible. For the analysis of sickness absence spells, we used generalized logit regression model that allowed us to study the risk of having at least one absence spell but also the increase of risk associated with the number of absence spells. We also took important covariates into account, some related to the physical work environment, but also age and occupational characteristics (occupation and economic activity of the company). Our results on the associations between covariates (age, occupation, physical– chemical–biological–biomechanical exposures) and sickness absence were consistent with the literature [Prins and de Graaf, 1986; Messing et al., 1998; Boedeker, 2001; Fuhrer et al., 2002] and confirmed the validity of our results. Additional results exploring the differences in the associations between psychosocial work factors and sickness absence according to occupational groups suggested that
there may be very few differences in these associations. Consequently, most of the associations between psychosocial work factors and sickness absence spells and days may be similar for all occupational groups. This study also had some limitations. The study design was cross-sectional, consequently no causal conclusion could be drawn from the results. Furthermore, a reverse causation may be suspected, as sickness absence may also have an impact on psychosocial work environment. No information was available on the number of absence days for each absence spell, making the analysis of short or long spells impossible. Moreover, the information about sickness absence was based on selfadministered questionnaires, but studies reported a high agreement between self-reported sickness absence and information from official registers [Voss et al., 2008]. Furthermore, psychosocial work factors were not all based on validated questionnaires, but other studies underlined the validity and interest of using proxies [Karasek et al., 2007]. Some psychosocial work factors may have not been taken into account, like discrimination, role conflicts, quality of leadership or organizational justice, that were found as risk factors for sickness absence in other studies [Kivimaki et al., 2003a; Rugulies et al., 2007; Nyberg et al., 2008; Roelen et al., 2009]. In our study, we had no information about the presence of chronic diseases, so we were unable to test its potential impact on the association between psychosocial work factors and sickness absence. A reporting bias may be possible as both psychosocial work factors and sickness absence were self-reported; this might lead to an overestimation of the associations observed. Finally, a healthy worker effect may also be suspected (unhealthy employees may have changed to less exposed jobs or left the market), leading to an underestimation of the associations between exposures and sickness absence.
Sensitivity Analyses Additional analyses were performed with employment status (permanent vs. temporary position) as an additional covariate and the results were found to be unchanged. Additional analyses were also performed taking sample design and weights into account and provided similar results
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TABLE II. Associations Between Psychosocial Work Factors and Sickness Absence Spells for Men and Women (Each Psychosocial Work Factor Studied Separately) Men OR (95% CI) Low-skill discretion 1absence vs. 0 2 vs. 0 3 vs. 0 Low-decision authority 1vs. 0 2 vs. 0 3 vs. 0 Low-decision latitude 1vs. 0 2 vs. 0 3 vs. 0 High-psychological demands 1vs. 0 2 vs. 0 3 vs. 0 Low-social support (supervisors) 1vs. 0 2 vs. 0 3 vs. 0 Low-social support (colleagues) 1vs. 0 2 vs. 0 3 vs. 0 Low-social support 1vs. 0 2 vs. 0 3 vs. 0 Job strain 1vs. 0 2 vs. 0 3 vs. 0 Iso-strain 1vs. 0 2 vs. 0 3 vs. 0 Low esteem 1vs. 0 2 vs. 0 3 vs. 0 Job insecurity 1vs. 0 2 vs. 0 3 vs. 0 Low-job promotion 1vs. 0 2 vs. 0 3 vs. 0
Women OR (95% CI)
1.27 (1.19^1.35) 1.47 (1.32 -1.64) 2.12 (1.78^2.52)
1.18 (1.11^1.26) 1.56 (1.39^1.74) 1.86 (1.55^2.24)
1.26 (1.18^1.34) 1.64 (1.47^1.82) 2.26 (1.90^2.68)
1.17 (1.10^1.24) 1.42 (1.28^1.57) 1.66 (1.42^1.95)
1.30 (1.23^1.38) 1.62 (1.46^1.79) 2.49 (2.11^2.92)
NS
1.10 (1.03^1.17) 1.09 (0.98^1.21) 1.22 (1.03^1.45)
NS
NS NS
1.24 (1.15^1.33) 1.61 (1.43^1.80) 1.97 (1.67^2.32)
NS
NS
1.28 (1.20^1.37) 1.45 (1.31^1.61) 2.18 (1.88^2.52) 1.34 (1.25^1.43) 1.69 (1.52^1.88) 2.22 (1.92^2.57) 1.21 (1.12^1.30) 1.35 (1.20^1.51) 1.33 (1.14^1.55)
NS NS
1.34 (1.25^1.44) 1.81 (1.62^2.02) 2.38 (2.03^2.81)
1.37 (1.28^1.46) 1.72 (1.55^1.92) 2.35 (2.03^2.72)
1.38 (1.28^1.49) 1.66 (1.49^1.85) 2.47 (2.14^2.85)
1.49 (1.36^1.63) 1.80 (1.59^2.05) 2.74 (2.34^3.21)
1.40 (1.32^1.49) 1.87 (1.68^2.07) 3.47 (2.91^4.14)
1.29 (1.21^1.37) 1.80 (1.62^1.99) 2.33 (1.98^2.75)
1.50 (1.40^1.60) 1.89 (1.70^2.10) 2.64 (2.28^3.06)
1.41 (1.33^2.19) 1.98 (1.79^2.19) 3.50 (2.95^4.16)
1.21 (1.12^1.30) 1.56 (1.38^1.76) 1.75 (1.47^2.08)
1.27 (1.20^1.35) 1.58 (1.42^1.75) 2.40 (2.03^2.83)
1.44 (1.32^1.56) 2.01 (1.77^2.28) 2.64 (2.20^3.16)
1.20 (1.12^1.29) 1.45 (1.30^1.62) 1.69 (1.44^1.98)
1.28 (1.20^1.38) 1.54 (1.38^1.70) 2.37 (2.04^2.74)
1.45 (1.36^1.56) 1.76 (1.58^1.95) 2.81 (2.42^3.26)
(Continued )
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TABLE II. (Continued.) Men OR (95% CI) Low reward 1vs. 0 2 vs. 0 3 vs. 0 Long working hours 1vs. 0 2 vs. 0 3 vs. 0 Night work 1vs. 0 2 vs. 0 3 vs. 0 Shift work 1vs. 0 2 vs. 0 3 vs. 0 Asocial workdays 1vs. 0 2 vs. 0 3 vs. 0 Low predictability 1vs. 0 2 vs. 0 3 vs. 0 Physical violence/sexual assault 1vs. 0 2 vs. 0 3 vs. 0 Bullying 1vs. 0 2 vs. 0 3 vs. 0 Verbal abuse 1vs. 0 2 vs. 0 3 vs. 0 Demands for responsibility 1vs. 0 2 vs. 0 3 vs. 0
Women OR (95% CI)
1.43 (1.35^1.52) 2.04 (1.83^2.26) 4.28 (3.52^5.18)
0.75 (0.67^0.84) 0.50 (0.40^0.64) 0.33 (0.20^0.53) NS 0.96 (0.87^1.07) 0.86 (0.72^1.03) 0.93 (0.72^1.22)
NS
NS NS
1.37 (1.08^1.73) 1.93 (1.40^2.68) 2.42 (1.56^3.74)
NS NS
NS NS
NS NS
0.75 (0.62^0.91) 0.78 (0.57^1.06) 0.52 (0.30^0.89) NS 1.04 (0.86^1.28) 0.86 (0.62^1.19) 1.08 (0.72^1.61) 1.24 (1.12^1.36) 1.29 (1.12^1.49) 1.38 (1.15^1.67) NS 1.05 (0.96^1.15) 0.99 (0.86^1.14) 1.11 (0.93^1.34) NS 1.00 (0.93^1.08) 1.12 (1.00^1.26) 1.05 (0.90^1.22)
NS NS
1.31 (1.22^1.40) 1.98 (1.78^2.20) 3.15 (2.69^3.70)
1.43 (1.33^1.54) 1.99 (1.78^2.23) 2.46 (2.08^2.92)
NS NS
NS NS
NS NS
NS NS
NS NS
1.73 (1.36^2.18) 1.87 (1.34^2.61) 2.42 (1.61^3.63)
NS NS
1.39 (1.29^1.50) 1.93 (1.73^2.16) 2.89 (2.50^3.34)
1.40 (1.30^1.51) 1.98 (1.78^2.21) 2.48 (2.14^2.87)
1.07 (1.00^1.15) 1.06 (0.95^1.18) 1.27 (1.09^1.47)
NS
NS 1.00 (0.94^1.07) 0.98 (0.88^1.08) 1.07 (0.90^1.26)
0.92 (0.86^0.98) 0.94 (0.84^1.04) 0.83 (0.70^0.98)
1.21 (1.12^1.30) 1.18 (1.05^1.34) 1.42 (1.18^1.70) NS 0.96 (0.90^1.04) 0.87 (0.77^0.99) 0.86 (0.71^1.04)
1.50 (1.41^1.61) 1.89 (1.70^2.10) 3.17 (2.70^3.72)
NS NS
Generalizedlogitregression analysis adjustedforage,occupation,economic activity,physical,chemical,biological, andbiomechanical exposures.Foreach variable,thefirst P-value provides the result of the global test,the second P-value is testing the difference between the first and the second category, and finally the third P-value is testing the difference between the second and the third category. NS, not significant. Bold: OR significant at 5%. P < 0.05. P < 0.01. P < 0.001 (chi-square and student test for the comparison between men and women).
for sickness absence spells, suggesting that our results may be generalized to the whole French population of employees (Supplementary Tables SI and SIII). Regarding sickness absence days, the results were different as the number of
significant MRs was strongly reduced from Table III to Supplementary Table SII and the MRs significant in Table V were no longer significant in the additional analyses (Supplementary Table SIV). This may be explained by two
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TABLE III. Associations Between Psychosocial Work Factors and Sickness Absence Days for Men and Women (Each Psychosocial Work Factor Studied Separately) Men MR Low-skill discretion Low-decision authority Low-decision latitude High-psychological demands Low-social support (Supervisors) Low-social support (Colleagues) Low-social support Job strain Iso-strain Low esteem Job insecurity Low-job promotion Low reward Long working hours Night work Shift work Asocial workdays Low predictability Physical violence/sexual assault Bullying Verbal abuse Demands for responsibility
1.09 1.09 1.09 1.09 1.15 1.03 1.22 1.13 1.16 1.22 1.10 1.16 1.22 1.20 1.00 1.06 1.05 1.00 1.37 1.18 1.10 1.04
Women 95% CI
(1.03^1.15) (1.03^1.15) (1.03^1.14) (1.03^1.14) (1.09^1.21) (0.98^1.09) (1.16^1.28) (1.06^1.19) (1.09^1.24) (1.16^1.29) (1.04^1.16) (1.10^1.22) (1.16^1.29) (1.08^1.33) (0.91^1.10) (0.99^1.12) (0.99^1.12) (0.95^1.06) (1.14^1.64) (1.11^1.25) (1.04^1.17) (0.98^1.09)
MR
95% CI
1.09 0.98 1.02 1.12 1.16 1.03 1.14 1.10 1.10 1.12 1.15 1.18 1.12 0.98 0.90 1.01 1.06 1.04 1.29 1.14 1.12 1.03
(1.03^1.15) (0.92^1.05) (0.97^1.09) (1.06^1.19) (1.10^1.24) (0.97^1.09) (1.07^1.20) (1.04^1.17) (1.03^1.17) (1.06^1.19) (1.08^1.21) (1.12^1.24) (1.06^1.18) (0.83^1.15) (0.77^1.06) (0.94^1.09) (0.98^1.14) (0.98^1.11) (1.09^1.53) (1.07^1.21) (1.06^1.19) (0.97^1.09)
Negative binomial regression analysis adjusted for age, occupation, economic activity, physical, chemical, biological, and biomechanical exposures. Bold: MR significant at 5%. P < 0.01. P < 0.001 (chi-square and student test for the comparison between men and women).
phenomena: (1) the MRs were very low even if significant in Table V; and (2) taking sample design and weights into account reduced the precision of the estimates and made them non-significant. Consequently, the results for sickness absence days should be taken with caution, as the associations may not be significant.
Comparison With the Literature Our results concerning the three Karasek’s job stress dimensions confirm the previous findings from the literature. In fact, low-decision latitude, high-psychological demands, and low-social support have already been observed as risk factors of sickness absence in other studies [Gimeno et al., 2004; Moreau et al., 2004; Niedhammer et al., 2008]. The two sub-dimensions of decision latitude, low skill discretion and low-decision authority, were also found to be risk factors for sickness absence for both genders [Väänänen et al., 2003]. Low reward was observed as a risk factor of sickness absence. Two other studies, among nurses’ aides and Danish
employees also reported the association of reward with sickness absence [Eriksen et al., 2003; Lund et al., 2005]. In agreement with one previous study [Niedhammer et al., 2013], the sub-dimensions of job insecurity and low-job promotion were risk factors for sickness absence. Low esteem was also found to be a risk factor in our study but to our knowledge, no previous study found an association between esteem and sickness absence. The following five working time variables were included in our study: long working hours, night work, shift work, asocial work days, and predictability. Long working hours was a protective factor of sickness absence spells. This result was also reported in the literature [Voss et al., 2001]. An explanation could be that long working hours may be associated with a high pressure at work and difficulty for workers to take sickness absence. However, we also found that long working hours increased the number of sickness absence days. Consequently, employees working more than 48 hr per week were less likely to take sickness absence, but if absent, they were more likely to take a longer duration of absence. Night work and asocial work days were
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TABLE IV. Associations Between Psychosocial Work Factors and Sickness Absence Spells for Men and Women (All Psychosocial Work Factors Studied Simultaneously) Men (N ¼ 24,464)
Women (N ¼18,110)
OR (95% CI) Low-decision latitude 1absence vs. 0 2 vs. 0 3 vs. 0 High-psychological demands 1vs. 0 2 vs. 0 3 vs. 0 Low-social support 1vs. 0 2 vs. 0 3 vs. 0 Low reward 1vs. 0 2 vs. 0 3 vs. 0 Long working hours 1vs. 0 2 vs. 0 3 vs. 0 Shift work 1vs. 0 2 vs. 0 3 vs. 0 Low predictability 1vs. 0 2 vs. 0 3 vs. 0 Physical violence/sexual assault 1vs. 0 2 vs. 0 3 vs. 0 Bullying 1vs. 0 2 vs. 0 3 vs. 0 Verbal abuse 1vs. 0 2 vs. 0 3 vs. 0 Demands for responsibility 1vs. 0 2 vs. 0 3 vs. 0
OR (95% CI)
1.13 (1.06^1.21) 1.37 (1.22^1.53) 1.68 (1.39^2.03) 1.04 (0.97^1.12) 1.07 (0.96^1.20) 1.02 (0.86^1.22) 1.06 (0.98^1.13) 1.05 (0.93^1.18) 1.15 (0.96^1.39)
NS NS NS NS NS NS NS
1.28 (1.19^1.38) 1.63 (1.44^1.84) 2.91 (2.33^3.64) 0.74 (0.66^0.84) 0.50 (0.39^0.64) 0.35 (0.20^0.58) 1.16 (1.07^1.26) 1.13 (0.99^1.29) 1.31 (1.07^1.59) 0.88 (0.82^0.94) 0.88 (0.79^0.99) 0.74 (0.62^0.89) 1.08 (0.84^1.39) 1.13 (0.78^1.62) 1.25 (0.76^2.04)
NS
NS NS
NS NS NS NS NS
1.14 (1.05^1.23) 1.34 (1.19^1.52) 1.59 (1.33^1.90) 1.11 (1.03^1.20) 1.12 (0.99^1.26) 1.42 (1.20^1.67) 1.11 (1.03^1.21) 1.16 (1.03^1.32) 1.22 (1.02^1.45) 1.31 (1.21^1.42) 1.40 (1.24^1.59) 1.90 (1.57^2.28) 0.73 (0.60^0.89) 0.76 (0.55^1.06) 0.50 (0.28^0.88) 1.19 (1.08^1.32) 1.16 (0.99^1.15) 1.30 (1.06^1.60) 0.94 (0.87^1.02) 1.02 (0.90^1.15) 0.85 (0.72^1.01)
1.26 (1.16^1.37) 1.56 (1.36^1.76) 1.51 (1.25^1.84)
0.96 (0.90^1.02) 0.92 (0.82^1.02) 0.94 (0.79^1.12)
NS NS NS NS
NS
NS
NS NS
NS
NS NS
NS NS NS NS NS
1.45 (1.13^1.86) 1.19 (0.83^1.70) 1.30 (0.84^2.03)
1.10 (1.01^1.19) 1.44 (1.27^1.63) 1.92 (1.59^2.30)
NS NS
1.12 (1.03^1.23) 1.40 (1.24^1.60) 1.78 (1.50^2.10) 1.20 (1.10^1.30) 1.55 (1.37^1.75) 1.59 (1.35^1.88) 1.01 (0.93^1.09) 0.96 (0.85^1.08) 1.06 (0.90^1.24)
NS NS NS NS
Generalizedlogitregression analysis adjustedforage,occupation,economic activity,physical,chemical,biological, andbiomechanical exposures.Foreach variable,thefirst P-value provides the result of the global test,the second P-value is testing the difference between the first and the second category, and finally the third P-value is testing the difference between the second and the third category. NS, not significant. Bold: OR significant at 5%. P < 0.05. P < 0.01. P < 0.001 (chi-square and student test for the comparison between men and women).
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TABLE V. Associations Between Psychosocial Work Factors and Sickness Absence Days for Men and Women (All Psychosocial Work Factors Studied Simultaneously) Men (N ¼ 7,191)
Low-decision latitude High-psychological demands Low-social support Low reward Long working hours Shift work Low predictability Physical violence/sexual assault Bullying Verbal abuse Demands for responsibility
Women (N ¼ 6,416)
MR
95% CI
MR
95% CI
1.02 0.99 1.15 1.08 1.20 1.01 0.98 1.31 1.11 0.99 1.01
(0.96^1.08) (0.93^1.05) (1.08^1.22) (1.02^1.15) (1.07^1.34) (0.94^1.08) (0.92^1.04) (1.08^1.59) (1.04^1.18) (0.93^1.06) (0.96^1.07)
0.99 1.06 1.09 1.04 1.00 0.90 1.01 1.16 1.08 1.03 1.00
(0.93^1.05) (1.00^1.13) (1.02^1.16) (0.97^1.11) (0.84^1.19) (0.91^1.07) (0.95^1.08) (0.97^1.38) (1.01^1.16) (0.96^1.10) (0.94^1.06)
Negative binomial regression analysis adjusted for age, occupation, economic activity, physical, chemical, biological, and biomechanical exposures. Bold: MR significant at 5%. P < 0.05. P < 0.01. P < 0.001 (chi-square and student test for the comparison between men and women).
not found to be associated with absence spells or days. Concerning night work, another study found no significant association between night work and sickness absence [Niedhammer et al., 2013]. Regarding shift work, an association was found with sickness absence spells for men but the rare previous studies provided inconclusive results [Kleiven et al., 1998; Tüchsen et al., 2008; Böckerman and Laukkanen, 2010; Niedhammer et al., 2013]. Finally, low predictability was a protective factor for sickness absence spells among men, something quite unexpected but to our knowledge, no previous study investigated the association between predictability of schedules and sickness absence. For the three factors related to workplace violence, bullying and physical violence/sexual assault were observed as risk factors of sickness absence in our study, confirming the findings from previous studies [Kivimaki et al., 2000; Vingård et al., 2005; Rugulies et al., 2007]. Verbal abuse also increased the risk of sickness absence in our study but to our knowledge, no previous study studied the association of verbal abuse with sickness absence. Verbal abuse may be close to bullying. Indeed, some items of bullying may also be possible situations of verbal abuse. The two factors were found to be associated with sickness absence spells, but bullying was observed as a risk factor of sickness absence days but not verbal abuse. Although associated, these two factors might thus measure different aspects of workplace violence. Demands for responsibility were not associated with sickness absence spells or days and to our knowledge, there have been no previous studies exploring this factor.
In the final model with all psychosocial work factors included simultaneously (Models II); the significant associations were independent of the other psychosocial work factors taken into account. The associations between psychosocial work factors are not necessarily well known and some factors may be causes or consequences of other factors [Rugulies et al., 2007, 2010]. This is why studying each factor separately (Models I) without adjusting for all factors together may also be interesting. The significant associations found in Models II were also observed when studying each factor separately, but some additional associations were found: high-psychological demands, low-social support, and physical violence/sexual assault were found to be risk factors for sickness absence spells among men and low-decision latitude, high-psychological demands for men, low reward, physical violence/sexual assault for women, and verbal abuse for both genders were found to be risk factors for sickness absence days. In our study, a lower number of significant associations was found with sickness absence days; indeed, some factors were associated with absence spells but not with the duration of sickness absence. This result can be explained by the fact that the duration of sickness absence was studied among those who had at least one absence day, so this analysis was based on a sub-sample while the study of sickness absence spells was based on the whole sample. Such analyses made possible to disentangle the factors that may have an association with sickness absence spells and those that may be associated with absence days. A previous study using an European sample found similar results [Niedhammer et al., 2013].
Psychosocial Factors at Work and Sickness Absence
CONCLUSION Our findings illustrate the importance of psychosocial work factors as risk factors of sickness absence, but their role may be more limited in the duration of absence. This study adds to the body of evidence that psychosocial work environment, not only the well-known factors but also more recent factors, should be a target for the prevention of sickness absence and the improvement of health at work.
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